Improved approximation of interactive dynamic influence diagrams using discriminative model updates

Interactive dynamic influence diagrams (I-DIDs) are graphical models for sequential decision making in uncertain settings shared by other agents. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. We formalize the concept of a minimal model set, which facilitates qualitative comparisons between different approximation techniques. We then present a new approximation technique that minimizes the space of candidate models by discriminating between model updates. We empirically demonstrate that our approach improves significantly in performance on the previous clustering based approximation technique.